As already mentioned, the neural network architecture was not a focus in this work, since we was interested in understanding how to manipulate the stability of the reconstructor by acting on the conditioning of the problem, with fixed architecture. Moreover, in Appendix  we showed that the instability phenomenon is unrelated with the architecture used to solve it, since it only depends on the ill-conditioning of the matrix \(A\)  defining the forward problem and the accuracy that the reconstructor achieve. Consequently, we decided to define each network to be a slight modification of the original UNet [1]} architecture, where we added a residual connection between the input and the output of the network, following what we already done in [2]}, [3]}.
